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    ABSTRACT - Kaushik Mishra (Cache)

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    basab
    This paper proposes a novel scalable cache miss handling architecture (MHA) design for high-memory level parallelism (MLP) processors. The design introduces a hierarchical MHA with small per-bank Miss Status Holding Registers (MSHRs) and a larger shared MSHR, using a Bloom filter to reduce searches. This results in a high performance, area-efficient design. It provides speedups of 32%, 50%, and 95% over a state-of-the-art MHA for SPECint, SPECfp, and multiprogrammed workloads respectively, and 1-18% and 10-21% over extrapolated alternatives consuming the same area.

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    ABSTRACT - Kaushik Mishra (Cache)

    Uploaded by

    basab
    24 views1 page
    This paper proposes a novel scalable cache miss handling architecture (MHA) design for high-memory level parallelism (MLP) processors. The design introduces a hierarchical MHA with small per-bank Miss Status Holding Registers (MSHRs) and a larger shared MSHR, using a Bloom filter to reduce searches. This results in a high performance, area-efficient design. It provides speedups of 32%, 50%, and 95% over a state-of-the-art MHA for SPECint, SPECfp, and multiprogrammed workloads respectively, and 1-18% and 10-21% over extrapolated alternatives consuming the same area.

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    Kaushik Mishra

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    ABSTRACT- Kaushik Mishra (Cache)

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    This paper proposes a novel scalable cache miss handling architecture (MHA) design for high-memory level parallelism (MLP) processors. The design introduces a hierarchical MHA with small per-bank Miss Status Holding Registers (MSHRs) and a larger shared MSHR, using a Bloom filter to reduce searches. This results in a high performance, area-efficient design. It provides speedups of 32%, 50%, and 95% over a state-of-the-art MHA for SPECint, SPECfp, and multiprogrammed workloads respectively, and 1-18% and 10-21% over extrapolated alternatives consuming the same area.

    Copyright:

    Attribution Non-Commercial (BY-NC)
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    24 views1 page

    ABSTRACT - Kaushik Mishra (Cache)

    Uploaded by

    basab
    This paper proposes a novel scalable cache miss handling architecture (MHA) design for high-memory level parallelism (MLP) processors. The design introduces a hierarchical MHA with small per-bank Miss Status Holding Registers (MSHRs) and a larger shared MSHR, using a Bloom filter to reduce searches. This results in a high performance, area-efficient design. It provides speedups of 32%, 50%, and 95% over a state-of-the-art MHA for SPECint, SPECfp, and multiprogrammed workloads respectively, and 1-18% and 10-21% over extrapolated alternatives consuming the same area.

    Copyright:

    Attribution Non-Commercial (BY-NC)
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
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    Scalable Cache Miss Handling for High Memory-Level Parallelism

    Recently-proposed processor microarchitectures for high Memory Level Parallelism (MLP) promise substantial performance gains. Unfortunately, current cache hierarchies have Miss-Handling Architectures (MHAs) that are too limited to support the required MLP they need to be redesigned to support 1-2 orders of magnitude more outstanding misses. Yet, designing scalable MHAs is challenging: designs must minimize cache lock-up time and deliver high bandwidth while keeping the area consumption reasonable. This paper presents a novel scalable MHA design for high-MLP processors. Our design introduces two main innovations. First, it is hierarchical, with a small MSHR(Miss Status Holding Register) file per cache bank, and a larger MSHR file shared by all banks. Second, it uses a Bloom filter to reduce searches in the larger MSHR file. The result is a high performance, area-efficient design. Compared to a stateof-the-art MHA on a high-MLP processor, our design speeds-up some SPECint, SPECfp, and multiprogrammed workloads by a geometric mean of 32%, 50%, and 95%, respectively. Moreover, compared to two extrapolations of current MHA designs, namely a large monolithic MSHR file and a large banked MSHR file, all consuming the same area, our design speeds-up the workloads by a geometric mean of 1-18% and 10-21%, respectively. Finally, our design performs very close to an unlimited-size, ideal MHA.

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